In this study, we propose methods for the automatic detection of photospheric
features (bright points and granules) from ultra-violet (UV) radiation, using a feature-based
classifier. The methods use quiet-Sun observations at 214 nm and 525 nm images taken
by Sunrise on 9 June 2009. The function of region growing and mean shift procedure are
applied to segment the bright points (BPs) and granules, respectively. Zernike moments of
each region are computed. The Zernike moments of BPs, granules, and other features are
distinctive enough to be separated using a support vector machine (SVM) classifier.
The size distribution of BPs can be fitted with a power-law slope −1.5. The peak value
of granule sizes is found to be about 0.5 arcsec^2. The mean value of the filling factor of
BPs is 0.01, and for granules it is 0.51. There is a critical scale for granules so that small
granules with sizes smaller than 2.5 arcsec^2 cover a wide range of brightness, while the
brightness of large granules approaches unity. The mean value of BP brightness fluctuations
is estimated to be 1.2, while for granules it is 0.22. Mean values of the horizontal velocities
of an individual BP and an individual BP within the network were found to be 1.6 km s^{−1} and
0.9 km s^{−1}, respectively. We conclude that the effect of individual BPs in releasing energy to
the photosphere and maybe the upper layers is stronger than what the individual BPs release
into the network.